{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import GridSearchCV  \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>GrocerySales</th>\n",
       "      <th>season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>8497.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Day  GrocerySales  season\n",
       "0    1       8418.59       1\n",
       "1    2       8447.47       1\n",
       "2    3       8336.49       1\n",
       "3    4       8579.17       1\n",
       "4    5       8524.31       1\n",
       "5    6       8623.50       1\n",
       "6    7       8320.11       1\n",
       "7    8       8313.53       1\n",
       "8    9       8461.34       1\n",
       "9   10       8497.24       1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = 'Train4.csv'\n",
    "df = pd.read_csv(file_name)\n",
    "test=pd.read_csv('Test2.csv')\n",
    "x_test=test.values\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['season'].value_counts()\n",
    "x=0\n",
    "num=1\n",
    "df[\"date\"]=0\n",
    "for i in df[\"season\"]:\n",
    " \n",
    "  if x!=0:\n",
    "    if df[\"season\"][x]==df[\"season\"][x-1] or df['season'][x]==1:\n",
    "     df[\"date\"][x]=num\n",
    "     num=num+1\n",
    "    else:\n",
    "      num=1\n",
    "      df[\"date\"][x]=num\n",
    "  x=x+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=df[['Day','date','season','GrocerySales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>14</th>\n",
       "      <td>15</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>8629.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>8840.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>8768.17</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>8719.43</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>8481.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>8383.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8343.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8333.17</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8497.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8326.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8542.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8544.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8330.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>8005.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>8269.73</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>8385.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>8259.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>8411.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>7967.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Day  date  season  GrocerySales\n",
       "0     1     0       1       8418.59\n",
       "1     2     1       1       8447.47\n",
       "2     3     2       1       8336.49\n",
       "3     4     3       1       8579.17\n",
       "4     5     4       1       8524.31\n",
       "5     6     5       1       8623.50\n",
       "6     7     6       1       8320.11\n",
       "7     8     7       1       8313.53\n",
       "8     9     8       1       8461.34\n",
       "9    10     9       1       8497.24\n",
       "10   11    10       1       8674.65\n",
       "11   12    11       1       8354.63\n",
       "12   13    12       1       8526.67\n",
       "13   14    13       1       8767.31\n",
       "14   15    14       1       8689.53\n",
       "15   16    15       1       8623.62\n",
       "16   17    16       1       8527.89\n",
       "17   18    17       1       8450.51\n",
       "18   19    18       1       8629.84\n",
       "19   20    19       1       8840.12\n",
       "20   21    20       1       8768.17\n",
       "21   22    21       1       8891.15\n",
       "22   23    22       1       8851.80\n",
       "23   24    23       1       8734.27\n",
       "24   25    24       1       8719.43\n",
       "25   26    25       1       8481.94\n",
       "26   27    26       1       8383.21\n",
       "27   28     1       2       8343.63\n",
       "28   29     1       2       8333.17\n",
       "29   30     2       2       8497.34\n",
       "30   31     3       2       8326.74\n",
       "31   32     4       2       8542.81\n",
       "32   33     5       2       8544.43\n",
       "33   34     6       2       8330.00\n",
       "34   35     7       2       8005.84\n",
       "35   36     8       2       8269.73\n",
       "36   37     9       2       8385.14\n",
       "37   38    10       2       8259.88\n",
       "38   39    11       2       8411.89\n",
       "39   40    12       2       7967.50"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(40)#Just to get a clear picture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Day  date  season\n",
       "0  693     1       1\n",
       "1  694     2       1\n",
       "2  695     3       1\n",
       "3  696     4       1\n",
       "4  697     5       1\n",
       "5  698     6       1\n",
       "6  699     7       1\n",
       "7  700     8       1\n",
       "8  701     9       1\n",
       "9  702    10       1"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset[[col for col in dataset.columns if col not in ('GrocerySales')]].values\n",
    "y = dataset['GrocerySales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=dataset\n",
    "target='GrocerySales'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = [x for x in train.columns if x not in [target]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf=RandomForestRegressor()\n",
    "rf.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-439.9786348604698"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores =cross_val_score(rf,x,y,cv=5,scoring ='neg_root_mean_squared_error')\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x241b1a387b8>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot([X for X in range(0,692)],y,'g')\n",
    "plt.plot([X for X in range(692,782)],a,'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#though score is good the plot isnt Good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightgbm import LGBMRegressor\n",
    "lgb=LGBMRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
       "              importance_type='split', learning_rate=0.1, max_depth=-1,\n",
       "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
       "              n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
       "              random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
       "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=lgb.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-465.49750417079593"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores =cross_val_score(lgb,x,y,cv=5,scoring ='neg_root_mean_squared_error')\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x241af0a8a20>]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot([X for X in range(0,692)],y,'g')\n",
    "plt.plot([X for X in range(692,782)],a,'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This LGB is acting like shit, Submitted this but no good result\n",
    "#Dont just beleive the CV Alone..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost.sklearn import XGBRegressor\n",
    "xgb1 = XGBRegressor(\n",
    " learning_rate =0.5,\n",
    " n_estimators=1000,\n",
    " max_depth=8,\n",
    " min_child_weight=1,\n",
    " gamma=0,\n",
    " subsample=0.8,\n",
    " colsample_bytree=0.8,\n",
    " objective= 'reg:tweedie',\n",
    " nthread=4,\n",
    " scale_pos_weight=1,\n",
    " seed=27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster=None, colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.8, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints=None,\n",
       "             learning_rate=0.5, max_delta_step=0, max_depth=8,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=1000, n_jobs=4, nthread=4, num_parallel_tree=1,\n",
       "             objective='reg:tweedie', random_state=27, reg_alpha=0,\n",
       "             reg_lambda=1, scale_pos_weight=1, seed=27, subsample=0.8,\n",
       "             tree_method=None, validate_parameters=False, verbosity=None)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb1.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=xgb1.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-485.301663183842"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores =cross_val_score(xgb1,x,y,cv=5,scoring ='neg_root_mean_squared_error')\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x241af2b0e48>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot([X for X in range(0,692)],y,'g')\n",
    "plt.plot([X for X in range(692,782)],a,'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x241af329978>]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Closer look\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot([X for X in range(600,692)],y[600:],'g')\n",
    "plt.plot([X for X in range(692,782)],a,'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this looks fine.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8884.08  , 8789.895 , 8973.369 , 8821.891 , 8807.902 , 8712.861 ,\n",
       "       8712.911 , 8793.231 , 8807.348 , 8973.197 , 8711.3   , 8848.179 ,\n",
       "       8917.729 , 8862.146 , 8929.763 , 8922.391 , 8820.848 , 8886.368 ,\n",
       "       8921.183 , 8892.93  , 8979.036 , 8931.551 , 8829.668 , 8945.548 ,\n",
       "       8869.215 , 8924.313 , 8884.08  , 8789.895 , 8973.369 , 8793.416 ,\n",
       "       8835.497 , 8797.006 , 8683.215 , 8756.861 , 8871.432 , 8739.108 ,\n",
       "       8780.427 , 8787.883 , 8697.121 , 8824.532 , 8904.761 , 8929.269 ,\n",
       "       8863.212 , 8887.725 , 8885.605 , 8907.1045, 8829.752 , 8861.064 ,\n",
       "       8796.217 , 8898.631 , 8780.511 , 9003.268 , 8861.674 , 9027.857 ,\n",
       "       8842.526 , 8793.416 , 8835.497 , 8797.006 , 8683.215 , 8798.852 ,\n",
       "       8767.842 , 8761.807 , 8751.952 , 8828.388 , 8738.524 , 8766.003 ,\n",
       "       8934.516 , 8903.47  , 8930.802 , 8880.743 , 8890.708 , 8914.191 ,\n",
       "       8833.104 , 8879.862 , 8865.984 , 8972.051 , 8896.17  , 8940.346 ,\n",
       "       8900.159 , 8872.515 , 8862.873 , 8790.716 , 8643.145 , 8742.025 ,\n",
       "       8759.317 , 8798.852 , 8767.842 , 8761.807 , 8751.952 , 8828.388 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "op2 = pd.DataFrame(columns = [\"GrocerySales\"])\n",
    "op2[\"GrocerySales\"]=a\n",
    "# op2[\"GrocerySales\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "op2.to_excel('xgb1.xlsx', index = False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
